3-dimensional visualisation is useful for the purpose of guiding endoscopic surgery, in planning and rehearsing of surgical techniques and in planning radiotherapy treatments, for example to more accurately target cancerous growths, and also in diagnosis; early detection of subtle structural changes within anatomical regions allows early treatment.
However it is also useful in understanding the anatomy of healthy human or animal bodies.
It has been known for some time that nuclear magnetic resonance (NMR or MR) is sensitive to different biological tissues.
Modern techniques can produce high resolution (MR) images in any anatomical plane either as 2D tomographic or 3D volume data.
Conventionally MR image data is viewed by a clinician on a slice by slice basis. Consecutive slices are printed onto a film and viewed by a light box. The interpretation of the data displayed in this way requires significant skill and even experts may miss subtle variations between image slices.
It is therefore desirable to visualise the MR data directly in 3 dimensions and techniques have been developed to enable this. Several stages are involved. The raw data is first acquired by MR methods. This data undergoes pre-processing techniques including noise filtering and particularly image data classification and/or segmentation. After pre-processing the data is rendered before it can be visualised on an output display. The present invention is particularly concerned with the pre-processing stage of classification (assigning a tissue type to each voxel or pixel) and/or image segmentation (dividing the image into spatial regions). This step essentially clarifies the structures of interest. Known techniques for this stage are either too slow to be of value in real-time applications or require used interaction.
Known rendering techniques are described for example in H. Fuchs, M. Kedem and S. P. Uselton, “Optimal Surface Reconstruction for Planar Contours”, Communications of the ACM, 20(10), pp. 693-702, 1977. W. E. Lorensen and H. E. Cline, “Marching Cubes: A High Resolution 3D Surface Construction Algorithm”, Computer Graphics, 21(4), pp. 163-169, 1987. R. A Drebin, L. Carpenter and P. Hanrahan, “Volume Rendering”, Computer Graphics, 22(4), pp. 65-74, 1988. L. A. Zadeh, “Fuzzy sets”, Information Control, 8, pp.338-353, 1965.
Given accurately segmented data, surface rendering methods exist to produce a 3D image visualisation. Similarly volume rendering methods may be employed, however these methods do not necessarily demand such rigorous segmentation, instead it is possible to classify the data according to the different tissue types. This is effectively pattern recognition but it only gives rough and ready view and needs refining to produce satisfactory visualisation.
It is possible to volume render classified data directly but there is too much information—all the different tissue types are shown and the actual structures obscured. Volume rendering allows the depiction of fuzzy surfaces (ie, it is possible to “see through” structures).
Known techniques for classification of data require some approximation assumptions and also user interaction. Typical is what is known as the supervised techniques which require supervisory interaction to identify tissue types (for example using a training data set), and assume that the distribution of the data is of a known type eg Gaussian. Such supervision is disadvantageous and impractical in a real time situation An unsupervised technique which is totally data driven is desirable.
Unsupervised, non-parametric approaches which are completely data driven are known and are advantageous since they do not require expert interaction. However, the hitherto known techniques are very slow because the computation required for segmentation of the data is extremely intensive. For the type of large data sets involved in a 3-dimensional image of an anatomical area, processing times of several hours are experienced and this is obviously impractical for real-time visualisation.